Vehicle monitoring system that dynamically varies data acquisition

ABSTRACT

Systems and methods are provided for retrieving values from a data bus of a vehicle. One illustrative method includes applying values from sensors at a vehicle to addresses of a data bus at the vehicle, retrieving values from the addresses of the data bus based on an operational state of the vehicle, and determining that the vehicle has entered another operational state. The method further includes loading a second sampling scheme corresponding with the other operational state, and retrieving values from an address of the data bus at a new rate corresponding with the other operational state.

FIELD

The disclosure relates to the field of vehicles, and in particular, tomonitoring the health of vehicles.

BACKGROUND

Vehicle health monitoring is a process by which sensor and other datadescribing a vehicle is retrieved and analyzed. Such analysis mayfacilitate an understanding of the viability of individual systemswithin the vehicle, or even the health of the vehicle as a whole.Vehicle health monitoring may also provide information that enablespredictive maintenance of various components of the vehicle. Forexample, vehicle health monitoring may facilitate scheduling for bothmaintenance of parts and replacement of parts at the vehicle.

Present vehicle health monitoring systems for aircraft track apredetermined number of parameters. However, the amount of bandwidthavailable for tracking parameters is limited. This lack of bandwidth inturn limits the number of parameters that may be sampled, as well as thesampling rates of these parameters. In aircraft that may be monitoredusing hundreds of thousands of parameters, this limitation isproblematic.

Therefore, it would be desirable to have a method and apparatus thattake into account at least some of the issues discussed above, as wellas other possible issues.

SUMMARY

Embodiments described herein dynamically select which parameters torecord, and/or rates at which to retrieve values for those parameters,based on the operational state of the vehicle and/or its individualsystems. As used herein, an “operational state” is a conditionexperienced by the vehicle or one of its systems, such as a conditionexperienced while the vehicle is actively operating. The dynamictechniques used by the embodiments described herein allow for a muchwider variety of parameters to be tracked during vehicle operations, andwithout exceeding existing bandwidth limitations in a computer thatmanages the vehicle. Furthermore, these dynamic techniques allow fortargeted and granular analysis of individual systems.

One embodiment is a system that includes a data bus at a vehicle,sensors at the vehicle which provide values that are applied toaddresses of the data bus such that each address of the data buscorresponds with a different sensor at the vehicle, and a vehiclemonitoring system coupled with the data bus via a communication channel.The vehicle monitoring system includes a controller, implemented by aprocessor, that identifies sampling schemes stored in a memory that eachdefine rates at which values are retrieved from the addresses of thedata bus and stored in records, and that each correspond with adifferent operational state of the vehicle. The controller retrievesvalues from the addresses of the data bus via the communication channelbased on a first sampling scheme that corresponds with an operationalstate and is stored in the memory, determines that the vehicle hasentered another operational state based on the records, loads a secondsampling scheme that corresponds with the other operational state and isstored in the memory, and retrieves values from an address of the databus via the communication channel at a new rate defined by the secondsampling scheme.

A further embodiment is a method that includes applying values fromsensors at a vehicle to addresses of a data bus at the vehicle,retrieving values from the addresses of the data bus based on anoperational state of the vehicle, and determining that the vehicle hasentered another operational state. The method further includes loading asecond sampling scheme corresponding with the other operational state,and retrieving values from an address of the data bus at a new ratecorresponding with the other operational state.

A further embodiment is a non-transitory computer readable mediumembodying programmed instructions which, when executed by a processor,are operable for performing a method. The method includes applyingvalues from sensors at a vehicle to addresses of a data bus at thevehicle, retrieving values from the addresses of the data bus based onan operational state of the vehicle, and determining that the vehiclehas entered another operational state. The method further includesloading a second sampling scheme corresponding with the otheroperational state, and retrieving values from an address of the data busat a new rate corresponding with the other operational state.

Other illustrative embodiments (e.g., methods and computer-readablemedia relating to the foregoing embodiments) may be described below. Thefeatures, functions, and advantages that have been discussed can beachieved independently in various embodiments or may be combined in yetother embodiments further details of which can be seen with reference tothe following description and drawings.

DESCRIPTION OF THE DRAWINGS

Some embodiments of the present disclosure are now described, by way ofexample only, and with reference to the accompanying drawings. The samereference number represents the same element or the same type of elementon all drawings.

FIG. 1 is a block diagram of a vehicle monitoring system in anillustrative embodiment.

FIG. 2 is a table illustrating a parameter list in an illustrativeembodiment.

FIG. 3 is a table illustrating operational states of a vehicle in anillustrative embodiment.

FIG. 4 is a table illustrating a sampling scheme defining rates forretrieving values from one or more addresses of a data bus in anillustrative embodiment.

FIG. 5 is a flowchart illustrating a method for operating a vehiclemonitoring system in an illustrative embodiment.

FIG. 6 is a flowchart illustrating details for utilizing an optimizationroutine to dynamically adjust rates for retrieving values in anillustrative embodiment.

FIG. 7 is a flow diagram of aircraft production and service methodologyin an illustrative embodiment.

FIG. 8 is a block diagram of an aircraft in an illustrative embodiment.

DESCRIPTION

The figures and the following description illustrate specificillustrative embodiments of the disclosure. It will thus be appreciatedthat those skilled in the art will be able to devise variousarrangements that, although not explicitly described or shown herein,embody the principles of the disclosure and are included within thescope of the disclosure. Furthermore, any examples described herein areintended to aid in understanding the principles of the disclosure, andare to be construed as being without limitation to such specificallyrecited examples and conditions. As a result, the disclosure is notlimited to the specific embodiments or examples described below, but bythe claims and their equivalents.

FIG. 1 is a block diagram of a vehicle 100 equipped with a vehiclemonitoring system 150 in an illustrative embodiment. According to FIG.1, vehicle monitoring system 150 receives input from sensors 102. Theinput describes a variety of systems of vehicle 100. In this embodiment,vehicle 100 comprises an aircraft, and the systems include flightcontrol surfaces 110, landing gear 112, flight recorder 114, anAuxiliary Power Unit (APU) such as APU 116, and environmental system 118(among others). A variety of sensors 102 may be installed in orproximate to the systems described above. For example, sensors 102 maycomprise pressure sensors, accelerometers, temperature sensors, currentsensors, voltage sensors, etc., that report values for a variety ofparameters describing the current state of systems 110-118. In someembodiments, sensors 102 may report tens or even hundreds of thousandsof parameters. Values determined by sensors 102 are applied to addresses122 at data bus 120 (e.g., an avionic data bus). Furthermore, valuesapplied to data bus 120 by systems 110-118 may be continuously updated.For example, an address 122 at data bus 120 storing a value for avoltage parameter may be updated as quickly as a sensor 102 can measurethat voltage (e.g., millions of times per second, or even faster). Thus,values applied to addresses 122 at data bus 120 may change withincredible rapidity. This is notable because data bus 120 does not storeprevious values for parameters. Hence, once a new value has been appliedto an address 122 at data bus 120, the previous value for that parameteris lost.

Vehicle management computer 130 periodically pulls values from data bus120 by operating an interface (I/F), such as I/F 132, to acquire valuesvia communication channel 146. I/F 132 may comprise a Serial AttachedSCSI (SAS) interface, Universal Serial Bus (USB) interface, etc.Communication channel 146 may comprise one or more wires coupled withdata bus 120 and I/F 132 that are capable of acquiring data words(“dwords”) from data bus 120. The acquisition of values from data bus120 may be directed by controller 134 based on instructions stored inmemory 136. Controller 134 stores values acquired from data bus 120 inrecords 142 at storage device 140 (e.g., a hard disk, flash memorydevice, non-volatile Random Access Memory (RAM), etc.) via an I/F 138(e.g., an Ethernet interface) and a communication channel 144 (e.g., acategory 5 (CAT5) cable). Controller 134 may be implemented, forexample, as custom circuitry, as a hardware processor executingprogrammed instructions, or some combination thereof.

Although it may be desirable to record every single value applied todata bus 120 over a period of time, such a large amount of bandwidth maynot be supported by communication channel 144 or storage device 140. Forexample, storage device 140 may be capable of writing less data persecond than is generated at data bus 120. In further embodiments,communication channel 144 may itself be incapable of transferring theamount of data generated at data bus 120 per second due to bandwidth orlatency issues. Hence, a technological bottleneck results wherein it isnot possible to fully store all values applied to data bus 120 at allpoints in time.

To account for this dilemma, vehicle monitoring system 150 is provided.Vehicle monitoring system 150 operates I/Fs 158 to dynamically acquireand process the values of parameters indicated by records 142. Based onthis information, controller 152 determines one or more operationalstates being experienced by vehicle 100 (e.g., flight characteristicssuch takeoff, landing, cruising, and health states for individualsystems; or an operational age of individual components withinindividual systems, etc.). These may include conditions experienced bythe vehicle 100 as a whole, and/or conditions experienced by individualsystems of the vehicle 100. Based on the current operational state(s) ofvehicle 100, controller 152 selects rates for retrieving variousparameters from addresses 122 at data bus 120. This may involve reducingretrieval rates for some systems and/or parameters in order to provideadditional bandwidth which is used to increase retrieval rates for othersystems and/or parameters which are of interest. In this manner,controller 152 is capable of dynamically altering what parameters(reported by values at corresponding bus addresses) are stored inrecords 142, and how quickly values for those parameters are retrieved.

Controller 152 may access the memory 154 in order to facilitate theoperations described above. In this embodiment, memory 154 includesparameter list 157, which indicates allowable ranges of retrieval ratesfor each parameter at data bus 120. Memory 154 additionally storesoperational states 156, which indicate what combinations of values maytrigger the beginning (or end) of an operational state. Memory 154 alsoincludes sampling schemes 155. Each sampling scheme 155 may definedesired retrieval rates for values from one or more addresses at databus 120. As used herein, the term “retrieval rate” refers to the rate atwhich values are acquired from data bus 120 by vehicle managementcomputer 130 for storage in records 142, and not to the rate at whichsensors 102 apply values to data bus 120.

Controller 152 may be implemented, for example, as custom circuitry, asa hardware processor executing programmed instructions, or somecombination thereof. While vehicle monitoring system 150 is presented asa separate computer system from vehicle management computer 130 in FIG.1, in further embodiments, vehicle monitoring system 150 is integratedinto vehicle management computer 130.

With a description of the physical components relating to vehiclemonitoring system 150 provided above, further discussion in FIGS. 2-4focuses upon various tables that may be stored in memory 154.

FIG. 2 is a table illustrating a parameter list 200 in an illustrativeembodiment. In this embodiment, parameter list 200 defines a variety ofparameters on a system-by-system basis, as well as a nominal retrievalrate, minimum retrieval rate, and maximum retrieval rate for eachparameter. Parameter list 200 may also be referred to as a constrainttable. Some parameters may have a minimum retrieval rate of zero ornull, indicating that they may go untracked for a period of time ifdesired. By providing a range of retrieval rates allowed for eachparameter (e.g., regardless of operational state), parameter list 200indicates the extent to which each parameter's retrieval rate may beadjusted when operational states change. Parameter list 200 may furtherindicate which address 122 at data bus 120 holds the value for eachparameter. Parameter list 200 may be generated during a design phaseprior to vehicle operation, and may utilize engineering knowledge andinsights relating to the various systems of vehicle 100.

FIG. 3 is a table illustrating operational states 300 of a vehicle in anillustrative embodiment. In this embodiment, operational states arecategorized based on whether they are linked to a specific system or tothe entire vehicle. Furthermore, operational states are each assigned apriority and a sampling scheme. If multiple operational states exist atthe same time (e.g., landing, and APU fluctuation), then retrieval ratesdefined in the sampling scheme for the more highly ranked operationalstate may trump retrieval rates defined in other applicable samplingschemes. In this embodiment, each operational state is also defined by atrigger, which indicates a combination of parameter values used todetermine whether the vehicle is in the operational state. For example,an operational state of cruising may be defined by records indicatingthat altitude is presently between twenty eight and thirty thousandfeet.

In further embodiments, the table of FIG. 3 also indicates processingresources to utilize at vehicle management computer 130 when the vehicle100 is in each of a variety of operational states. These techniques maybe valuable when attempting to provide reports, analyses, or summariesof critical parameters on a timely basis and with limited processingresources. In one embodiment, the table of FIG. 3 defines a processingintensity allowed for each of multiple operational states. For example,in order to increase processor reliability or reduce heat generation, alimited number of operations per unit of time may be allowed based uponthe current operational state of vehicle 100. One example would be tooperate a processor of vehicle management computer 130 at 25% of itsrated capacity during cruise, a state where parametric values do notchange rapidly. On the other hand, critical operations and maneuversoccur at a much faster rate during landing, and therefore, the processorspeed may be increased to 75% of its rated capacity. A second approachincludes specifying an increase in processor throughput allowed whenmonitoring a specific system during an anomalous condition. For example,if a flight management system behaves in a manner that is not expected,the processor throughput could be adjusted to include an additional 10%of rated capacity in order to facilitate capturing and processingadditional data.

Furthermore, for complex vehicle monitoring systems that use multiplealgorithms to model system health, vehicle monitoring system 150 maydefine a minimum, nominal, and maximum number of processor operationsper unit of time for each of these algorithms. Algorithms that monitormotor health may utilize various computing resources, and the quality ofthe results of these algorithms may depend on the amount of computingresources used. For instance, a motor winding prognosis algorithm whichprovides accurate estimates of remaining useful life may requiresophisticated modeling based on neural nets, and may utilize multipleprocessor cycles to complete. Motor winding down-state predictionalgorithms, on the other hand, may only utilize a low fidelity datapoint and a threshold check, and relatively few processor cycles.Processor utilization rates may be predefined when designing vehiclemonitoring system 150, and may be based on the computing capabilities ofvehicle management computer 130.

FIG. 4 is a table illustrating a sampling scheme 400 defining retrievalrates for one or more parameters in an illustrative embodiment. Memory154 stores multiple sampling schemes, such as one sampling scheme peroperational state. In this embodiment, sampling scheme 400 defines adesired retrieval rate for one or more parameters. Furthermore, samplingscheme 400 assigns a rank to each parameter. In this manner, parametersthat are assigned ranks of lower importance may have their samplingrates downgraded first. In environments where multiple operationalstates (and therefore multiple sampling schemes) apply at once tovehicle 100, rankings for parameters may be assigned according to thesampling scheme for the highest priority operational state.

Allowing each sampling scheme to rank parameters (and hencecorresponding addresses of data bus 120) differently provides atechnical benefit, because it ensures that data which is being recordedis the most relevant data at the time. For example, if landing gearparameters are not important during cruise, the system can allow thoseparameters to go untracked or at a downgraded rate of monitoring toaccommodate the needs of other parameters that need increased retrievalrates. That is, controller 134 may reduce a retrieval rate for oneaddress 122 at data bus 120, while increasing a retrieval rate foranother address 122 at data bus 120.

In summation, vehicle monitoring system 150 stores a variety ofinformation. For example, vehicle monitoring system 150 includesinformation describing the allowable range of retrieval rates for eachparameter. Vehicle monitoring system 150 also includes informationindicating what parameter values trigger different operational states,and what sampling schemes to load for different operational states.Based on these sampling schemes, vehicle monitoring system 150 mayselectively increase the retrieval rates of specific parameters tofacilitate granular tracking, while downgrading retrieval rates of otherparameters which are less important at the moment.

Illustrative details of the operation of vehicle monitoring system 150will be discussed with regard to FIG. 5. Assume, for this embodiment,that vehicle 100 has initialized, and that a default set of parametersis being tracked according to a default sampling scheme.

FIG. 5 is a flowchart illustrating a method 500 for operating a vehiclemonitoring system in an illustrative embodiment. The steps of method 500are described with reference to vehicle 100 of FIG. 1, but those skilledin the art will appreciate that method 500 may be performed in othervehicles as desired. The steps of the flowcharts described herein arenot all inclusive and may include other steps not shown. The stepsdescribed herein may also be performed in an alternative order.

As vehicle 100 continues to operate, values from sensors 102 at thevehicle are applied to addresses 122 of data bus 120 at the vehicle(step 502). Vehicle management computer 130 identifies sampling schemes155, which each define rates at which values are retrieved fromaddresses 122 of data bus 120 and stored in records 142 (step 504). Eachsampling scheme 155 corresponds with a different operational state ofvehicle 100.

Vehicle management computer 130 periodically retrieves values fromaddresses 122 at data bus 120, based on the operational state of thevehicle (e.g., based on a first of sampling schemes 155 correspondingwith the operational state of the vehicle), and proceeds to updaterecords 142 (step 506). During this process, controller 152 retrievesthe records 142 from storage device 140. As discussed above, records 142each indicate values of a parameter reported by at least one sensor of avehicle at a point in time. For example, each record may report one ormore values for a parameter acquired by vehicle management computer 130at different points in time.

Assume, for this embodiment, that vehicle 100 is an aircraft, and thatvehicle 100 is proceeding to take off from a runway. This results inchanges to values applied to addresses 122 at data bus 120, and thesechanged values are reflected in records 142 generated by vehiclemanagement computer 130. Based on the records 142, controller 152determines that vehicle 100 has entered another operational state (step508). For example, controller 152 may determine that current values of aset of parameters now indicate that vehicle 100 has entered anotheroperational state (e.g., take off), or left an operational state. Giventhat the operational state of vehicle 100 has changed, controller 152determines whether, and how, retrieval rates for various parametersshould be adjusted.

To this end, controller 152 loads a second of sampling schemes 155 thatcorresponds with the other operational state (step 510). Controller 152further retrieves values from an address 122 at data bus 120 at a newrate corresponding with the other operational state (e.g., a new ratedefined by a second of sampling schemes 155, which is the currentsampling scheme) (step 512). One or more rates indicated in a second ofsampling schemes 155 may use an increased amount of bandwidth atcommunication channel 144, and/or at storage device 140. In order toensure that no bottlenecking of data occurs, controller 152 may reduceretrieval rates of certain other parameters (at other addresses 122), orcease sampling those other parameters altogether. This may be performedso long as the minimum retrieval rate for each parameter (defined inparameter list 157) is maintained.

In some embodiments, controller 152 may load multiple sampling schemes155 (each corresponding with a different operational state) in responseto determining that vehicle 100 is subject to multiple operationalstates. In such embodiments, rates indicated in the sampling scheme forthe highest priority operating condition may be implemented, followed byrates indicated in the sampling scheme for the next highest priorityoperating condition. Parameters that do not have a defined retrievalrate in any loaded sampling schema may then have their retrieval ratesdowngraded to provide bandwidth for the other parameters. Controller 152then directs controller 134 to adjust how often values for parametersare acquired via I/F 132 and stored in records 142. Steps 508-512 maythen be repeated on an ongoing basis in order to dynamically adjust howsystems are monitored.

For instance, landing gear parameters may include vibration, position,temperatures, rotational rates and speeds, and tire pressures. This datamay be of little interest if the aircraft is in mid-flight at altitude.Vehicle monitoring system 150 may therefore define operational statesunder which landing gear parameters are valuable (during rollout,takeoff, landing, and taxi, in this case), and then increase retrievalrates of landing gear parameters at during those times. In anotherexample, retrieval rates for parameters relating to composite structuralvibration may be downgraded except for during high load conditions,during high angle maneuvers, or when pitch rate exceeds three degreesper second.

Method 500 may further comprise controller 134 generating andtransmitting new records (or a summary thereof) for review. For example,records 142 may be transformed into actionable information (e.g., areport), and presented to an appropriate user (e.g., a pilot,maintainer, engineer etc.) via an electronically displayed message, awork order, a text message, or any other suitable format. Thisactionable information may further indicate operational states thatexisted when records 142 were generated. Actionable information mayfacilitate decision-making processes related to shutting down systems,curtailing a flight, limiting the flight or mission envelope, decidingwhether to order new parts, deciding whether to perform a repair action,or deciding whether to post an alert regarding the remaining life of acomponent.

In short, method 500 allows for dynamically varying vehicle monitoringin order to collect, process and store rich data sets that are tailoredfor the current operational state of a vehicle. This yields morevaluable and accurate actionable information for users. Hence, method500 may help to reduce flight aborts, reduce schedule disruptions, andalso reduce maintenance time without the additional burden of a larger,more complex embedded computer system at vehicle 100.

FIG. 6 is a flowchart illustrating a method 600 for utilizing anoptimization routine to dynamically adjust sampling rates in anillustrative embodiment. FIG. 6 illustrates that these optimizationroutines may be implemented with predefined constraints and algorithmsfor different operational states. Furthermore, the processes describedherein may be implemented with linear or non-linear optimizationroutines. In this embodiment, the objective function and constraints foreach of various operational states are predefined.

According to method 600, an objective function is loaded by controller152 (step 602). An objective function is a formula that outputs a scorefor a given combination of retrieval rates for parameters. For example,objective functions may comprise linear functions that describerelationships between parameter values, system priorities, andprocessing constraints. In one embodiment, an objective function maylightly penalize reducing a retrieval rate of a specific parameter,while also heavily penalizing completely halting retrieval of values forthat parameter. Different combinations of retrieval rates are scoreddifferently by the objective function, and the resulting scores areutilized as a basis for determining how close a given set of retrievalrates is to a theoretical optimum.

In one embodiment, objective functions for each operational state aredefined and included in a table. For example, one objective function mayprovide a higher score for an increased retrieval rate of wing flapangle, while another objective function for a different operationalstate may provide a higher score for increasing the retrieval rate ofvoltage at an APU.

Controller 152 further defines constraints based on sampling schemes forthe current operational states (e.g., the loaded sampling schemes) (step604). These constraints include any required retrieval rates dictated bythe sampling schemes. Constraints may further include minimum or maximumretrieval rates defined by parameter list 200.

Controller 152 additionally loads an optimization routine (step 606). Anoptimization routine may comprise a deterministic or stochasticoptimization algorithm, such as algorithms for Monte Carlo optimization,simulated annealing, a Nelder-Mead simplex, etc. Different optimizationroutines may be utilized for different operational states. Theseroutines seek to maximize data collection/transmission rates within thegiven constraints while achieving the highest score via the objectivefunction.

With the optimization routine is loaded, controller 152 proceeds toconverge the optimization routine based on the objective function (step608). That is, controller 152 generates multiple different theoreticalcombinations of sampling rates for the parameters, and scores eachcombination of retrieval rates. Controller 152 may then iterativelygenerate new combinations of retrieval rates based on the scores of theprevious combinations of sampling rates. In this manner, controller 152may converge via linear or non-linear optimization techniques at anoptimal or near-optimal final combination of retrieval rates.

With a final combination of sampling rates determined based on theobjective function, controller 152 directs vehicle management computer130 to sample the parameters according to the final combination of rates(step 610).

EXAMPLES

Referring more particularly to the drawings, embodiments of thedisclosure may be described in the context of an aircraft manufacturingand service method 700 as shown in FIG. 7 and an aircraft 702 as shownin FIG. 8. During pre-production, illustrative method 700 may includespecification and design 704 of the aircraft 702 and materialprocurement 706. During production, component and subassemblymanufacturing 708 and system integration 710 of the aircraft 702 takesplace. Thereafter, the aircraft 702 may go through certification anddelivery 712 in order to be placed in service 714. While in service by acustomer, the aircraft 702 is scheduled for routine maintenance andservice 716 (which may also include modification, reconfiguration,refurbishment, and so on). Apparatus and methods embodied herein may beemployed during any one or more suitable stages of the production andservice method 700 (e.g., specification and design 704, materialprocurement 706, component and subassembly manufacturing 708, systemintegration 710, certification and delivery 712, service 714,maintenance and service 716) and/or any suitable component of aircraft702 (e.g., airframe 718, systems 720, interior 722, propulsion 724,electrical 726, hydraulic 728, environmental 730).

Each of the processes of method 700 may be performed or carried out by asystem integrator, a third party, and/or an operator (e.g., a customer).For the purposes of this description, a system integrator may includewithout limitation any number of aircraft manufacturers and major-systemsubcontractors; a third party may include without limitation any numberof vendors, subcontractors, and suppliers; and an operator may be anairline, leasing company, military entity, service organization, and soon.

As shown in FIG. 8, the aircraft 702 produced by illustrative method 700may include an airframe 718 with a plurality of systems 720 and aninterior 722. Examples of high-level systems 720 include one or more ofa propulsion system 724, an electrical system 726, a hydraulic system728, and an environmental system 730. Any number of other systems may beincluded. Although an aerospace example is shown, the principles of theinvention may be applied to other industries, such as the automotiveindustry.

As already mentioned above, apparatus and methods embodied herein may beemployed during any one or more of the stages of the production andservice method 700. For example, components or subassembliescorresponding to production stage 708 may be fabricated or manufacturedin a manner similar to components or subassemblies produced while theaircraft 702 is in service. Also, one or more apparatus embodiments,method embodiments, or a combination thereof may be utilized during theproduction stages 708 and 710, for example, by substantially expeditingassembly of or reducing the cost of an aircraft 702. Similarly, one ormore of apparatus embodiments, method embodiments, or a combinationthereof may be utilized while the aircraft 702 is in service, forexample and without limitation, to maintenance and service 716. Forexample, the techniques and systems described herein may be used forsteps 706, 708, 710, 714, and/or 716, and/or may be used for airframe718 and/or interior 722. These techniques and systems may even beutilized for systems 720, including for example propulsion 724,electrical 726, hydraulic 728, and/or environmental 730.

In one embodiment, vehicle monitoring system 150 is assembled into anaircraft in system integration 710, and then is utilized in service 714.During this time, vehicle monitoring system 150 continues to sampleparameters at dynamically varying rates, and to generate reports andanalysis as desired by users such as technicians and pilots. Inventivecomponents and methods may be utilized throughout component andsubassembly manufacturing 708 in order to manufacture new parts.

Any of the various control elements (e.g., electrical or electroniccomponents) shown in the figures or described herein may be implementedas hardware, a processor implementing software, a processor implementingfirmware, or some combination of these. For example, an element may beimplemented as dedicated hardware. Dedicated hardware elements may bereferred to as “processors”, “controllers”, or some similar terminology.When provided by a processor, the functions may be provided by a singlededicated processor, by a single shared processor, or by a plurality ofindividual processors, some of which may be shared. Moreover, explicituse of the term “processor” or “controller” should not be construed torefer exclusively to hardware capable of executing software, and mayimplicitly include, without limitation, digital signal processor (DSP)hardware, a network processor, application specific integrated circuit(ASIC) or other circuitry, field programmable gate array (FPGA), readonly memory (ROM) for storing software, random access memory (RAM),non-volatile storage, logic, or some other physical hardware componentor module.

Also, a control element may be implemented as instructions executable bya processor or a computer to perform the functions of the element. Someexamples of instructions are software, program code, and firmware. Theinstructions are operational when executed by the processor to directthe processor to perform the functions of the element. The instructionsmay be stored on storage devices that are readable by the processor.Some examples of the storage devices are digital or solid-statememories, magnetic storage media such as a magnetic disks and magnetictapes, hard drives, or optically readable digital data storage media.

Although specific embodiments are described herein, the scope of thedisclosure is not limited to those specific embodiments. The scope ofthe disclosure is defined by the following claims and any equivalentsthereof.

What is claimed is:
 1. A method comprising: applying values from sensorsat a vehicle to addresses of a data bus at the vehicle; retrievingvalues from the addresses of the data bus based on an operational stateof the vehicle; determining that the vehicle has entered anotheroperational state; and retrieving values from an address of the data busat a new rate corresponding with the other operational state.
 2. Themethod of claim 1 further comprising: storing new records that reportvalues from the address at the new rate.
 3. The method of claim 1further comprising: identifying sampling schemes that each define ratesat which values are retrieved from the addresses of the data bus andstored in records, and that each correspond with a different operationalstate of the vehicle, wherein: retrieving values from the addressesbased on the operational state of the vehicle is based on a firstsampling scheme corresponding with the operational state of the vehicle;retrieving values from an address of the data bus at the new rate isbased on a second sampling scheme corresponding with the otheroperational state.
 4. The method of claim 3 wherein: retrieving valuesfrom an address of the data bus at the new rate comprises increasing afirst rate at which values are retrieved from a second address of thedata bus to utilize additional bandwidth, and the method furthercomprises: decreasing a second rate at which values are retrieved from afirst address of the data bus in order to provide the additionalbandwidth on a communication channel coupled with the data bus.
 5. Themethod of claim 4 further comprising: dynamically selecting the firstaddress based on a rank assigned to a parameter that corresponds withthe first address.
 6. The method of claim 5 wherein: dynamicallyselecting the first address is performed via an optimization routine,wherein an objective function used by the optimization routine is basedon ranks assigned to parameters by the sampling scheme.
 7. The method ofclaim 3 further comprising: in response to determining that the vehicleis subject to multiple operational states: loading multiple samplingschemes that each correspond with a different one of the operationalstates; and retrieving values from multiple addresses of the data bus atmultiple new rates defined by the multiple sampling schemes.
 8. Themethod of claim 1 wherein: the operational states indicate conditionsexperienced by the vehicle as a whole, and conditions experienced byindividual systems of the vehicle.
 9. A non-transitory computer readablemedium embodying programmed instructions which, when executed by aprocessor, are operable for performing a method of: applying values fromsensors at a vehicle to addresses of a data bus at the vehicle;retrieving values from the addresses of the data bus based on anoperational state of the vehicle; determining that the vehicle hasentered another operational state; and retrieving values from an addressof the data bus at a new rate corresponding with the other operationalstate.
 10. The medium of claim 9, wherein the method further comprises:storing new records that report values from the address at the new rate.11. The medium of claim 9, wherein the method further comprises:identifying sampling schemes that each define rates at which values areretrieved from the addresses of the data bus and stored in records, andthat each correspond with a different operational state of the vehicle,wherein: retrieving values from the addresses based on the operationalstate of the vehicle is based on a first sampling scheme correspondingwith the operational state of the vehicle; retrieving values from anaddress of the data bus at the new rate is based on a second samplingscheme corresponding with the other operational state.
 12. The medium ofclaim 11, wherein: retrieving values from an address of the data bus atthe new rate comprises increasing a first rate at which values areretrieved from a second address of the data bus to utilize additionalbandwidth, and the method further comprises: decreasing a second rate atwhich values are retrieved from a first address of the data bus in orderto provide the additional bandwidth on a communication channel coupledwith the data bus.
 13. The medium of claim 12, wherein the methodfurther comprises: dynamically selecting the first address, based on arank assigned to a parameter that corresponds with the first address.14. The medium of claim 13, wherein: dynamically selecting the firstaddress is performed via an optimization routine, wherein an objectivefunction used by the optimization routine is based on the ranks assignedto parameters by the sampling scheme.
 15. The medium of claim 11,wherein the method further comprises: in response to determining thatthe vehicle is subject to multiple operational states at once: loadingmultiple sampling schemes that each correspond with a different one ofthe operational states; and retrieving values from multiple addresses ofthe data bus at multiple new rates defined by the multiple samplingschemes.
 16. The medium of claim 9, wherein: operational states indicateconditions experienced by the vehicle as a whole, and conditionsexperienced by individual systems of the vehicle.
 17. A systemcomprising: a data bus at a vehicle; sensors at the vehicle whichprovide values that are applied to addresses of the data bus such thateach address of the data bus corresponds with a different sensor at thevehicle; and a vehicle monitoring system coupled with the data bus via acommunication channel, the vehicle monitoring system comprising: acontroller, implemented by a processor, that identifies sampling schemesstored in a memory that each define rates at which values are retrievedfrom the addresses of the data bus and stored in records, and that eachcorrespond with a different operational state of the vehicle, thecontroller retrieves values from the addresses of the data bus via thecommunication channel based on a first sampling scheme that correspondswith an operational state and is stored in the memory, determines thatthe vehicle has entered another operational state based on the records,loads a second sampling scheme that corresponds with the otheroperational state and is stored in the memory, and retrieves values froman address of the data bus via the communication channel at a new ratedefined by the second sampling scheme.
 18. The system of claim 17wherein: the controller decreases a rate at which values are retrievedfrom a first address of the data bus in order to provide additionalbandwidth on a communication channel coupled with the data bus, andincreases a rate at which values are retrieved from a second address ofthe data bus to utilize the additional bandwidth.
 19. The system ofclaim 18 wherein: the controller dynamically selects the first address,based on a rank assigned to a parameter that corresponds with the firstaddress.
 20. The system of claim 19 wherein: the controller dynamicallyselects the first address via an optimization routine, wherein anobjective function used by the optimization routine is based on ranksassigned to parameters by the sampling scheme.
 21. The system of claim17 wherein: in response to determining that the vehicle is subject tomultiple operational states at once, the controller loads multiplesampling schemes that each correspond with a different one of theoperational states, and retrieves values from multiple addresses of thedata bus at multiple new rates defined by the multiple sampling schemes.22. The system of claim 17 wherein: the operational states indicateconditions experienced by the vehicle as a whole, and conditionsexperienced by individual systems of the vehicle.
 23. The system ofclaim 17 wherein: the vehicle is an aircraft, the data bus comprises anavionic data bus.